A hyperspectral imaging system operates to identify a targeted object solely based upon detection of energy reflected from or thermally generated by the object. The hyperspectral imaging system typically includes a linear field of view hyperspectral sensor containing a single pixel, or one or more rows of pixels, which can detect optical energy having wavelength bandwidths spanning, for example, the UV/visible through the MWIR and LWIR ranges. In addition, the hyperspectral imaging system usually includes target identification software that operates to automatically acquire, identify, classify and track a targeted object based on the spectral energy detected at the pixel or pixels of the sensor. For example, in a military application, a hyperspectral imaging system can differentiate between detected spectral characteristics representative of camouflage paint and foliage to determine that a military target is present in an imaged region. In addition, a hyperspectral imaging system can evaluate detected infrared spectral signatures to determine that certain biochemical agents are present within an imaged region.
Testing of early prior art hyperspectral imaging systems, which typically did not have any accompanying target identification software, primarily involved calibrating the sensor. In a calibration test, the radiometric performance of the sensor, which includes physical performance parameters such as sensitivity, resolution and spectral sensitivity, is evaluated. Based on the results of radiometric performance testing, the operation of sensors of different hyperspectral imaging systems could be compared.
With the improvement of imaging technologies, target identification software began to be included in a hyperspectral imaging system. Advanced hyperspectral imaging systems now include highly complex target identification software which has the capability to process the very large amounts of spectral data likely to be collected in current hyperspectral imaging system applications. For example, in a typical military application, the hyperspectral imaging system must be able to process the large amounts of spectral data that would be collected based on the presence of a countermeasure that is placed in an imaged region to conceal a targeted object and which causes the sensor to detect a plurality of spectral characteristics that normally would not be present in the imaged region. In addition, a substantial amount of spectral data must be processed to identify the presence of an object in an imaged region where the energy radiated from the object occupies less than the size of a pixel of a sensor of a hyperspectral imaging system. As the target software became an integral part of the hyperspectral imaging system, testing of a hyperspectral imaging system now required, in addition to calibrating the sensor, evaluating how well the target software identified or otherwise extracted target and position information from the spectral data collected by the sensor.
In the prior art, the complex target software is usually tested independently of the operation of the hyperspectral sensor itself. In common prior art testing of the target software, test software creates a digital representation of the spectral emissions or reflections expected to occur in a region containing a target and the digital representation is used to validate the target software. This isolated testing of the target software is not always adequate and accurate because it assumes (i) that the effects upon the sensor of any non-linear mixing of spectrum, which occurs when one object reflects optical energy onto another object, can be ignored; and (ii) that the sensor does not include an anomaly that would cause the targeting software to be less effective. Thus, the independent testing of the target software does not detect and compensate for any hyperspectral sensor anomaly, which can lead to potentially invalid test results concerning the overall operation of the hyperspectral imaging system.
In addition, laboratory or bench test scene generation systems that were developed in the prior art can project a predetermined scene including a target to a hyperspectral imaging system to provide that the sensor and the target software functionalities can be evaluated. Such prior art bench testing systems, however, only provide that a limited number of different scenes including a target can be projected and do not permit dynamic and arbitrary control of the spectral and spatial characteristics of a projected scene. See D. B. Beasley, D. A. Saylor, J. Buford, “Overview of Dynamic Scene Projectors at the U.S. Army Aviation and Missile Command,” Proc. SPIE Vol. 4717, pp. 136–147, April 2002, incorporated by reference herein. As a result, current bench testing techniques do not provide that the spectral and spatial characteristics of a projected scene can be sufficiently controlled, such that testing of a hyperspectral imaging system can be performed using a projected scene that simulates all of the spectral and spatial parameters that may exist in a natural scene.
In view of the limitations of the bench testing systems, actual field testing, such as placing a hyperspectral imaging system in an airplane and flying the system over a natural scene including a test target, has become a standard method for testing and demonstrating the performance of a hyperspectral imaging system. Such field testing, however, is costly and often is unrepeatable and uncontrollable. Moreover, in many circumstances, even a flight field test may not provide the variety or the correct background spectral signatures required to test an application of the hyperspectral imaging system.
Thus, the various prior art hyperspectral imaging system testing techniques cannot test the system repeatedly under controlled conditions that simulate the spectral and spatial characteristics expected to exist in a natural scene including a target, and that can also determine whether a spectral variation that sometimes may be present in a natural scene will cause the target software to fail to identify the target in such modified natural scene.
Therefore, a need exists for system and method for generating a projected linear scene having spectral and spatial characteristics that can be arbitrarily and dynamically controlled where the system and method can be implemented in a laboratory setting to perform repeatable testing of a hyperspectral imaging system for both radiometric and target identification software performance.